In a liquid chromatograph mass spectrometer (LC-MS), by repeating a scan measurement in a predetermined mass-to-charge ratio m/z range in a mass spectrometer as a detector, a mass spectrum indicating a relationship between a mass-to-charge ratio and a signal intensity can be obtained from moment to moment. Further, in a liquid chromatograph using a PDA detector as a detector, it is possible to obtain an absorption spectrum indicating a relationship between a wave number, a wavelength, etc., and a signal intensity (absorbance) from moment to moment. In this specification, data constituting spectrums such as a plurality of mass spectrums or absorption spectrums obtained according to changes of parameters such as time will be referred to as three-dimensional spectral data.
FIG. 9A is a schematic diagram of three-dimensional spectral data obtained by an LC-MS. Three-dimensional spectral data in this case denotes data having three dimensions: a mass-to-charge ratio m/z which is a unit axis of a mass spectrum; a signal intensity (ion intensity) of a mass spectrum; and a time (retention time RT).
By the way, in various fields such as biochemistry, food, and environmental fields, in order to search for characteristic components from a complicated sample including multiple components or to examine the content of the component, differential analysis by profiling (multivariate analysis) is used (see Non-Patent Document 1). In difference analysis using three-dimensional spectral data obtained from each sample, generally, features such as a peak height, a peak area, etc., are initially extracted from three-dimensional spectral data to create characteristic data. Then, for the two-dimensional characteristic data table in which the characteristic data created for each sample is arranged in a table format, multivariate analysis such as principle component analysis is performed, and from the result, the similarities, etc., of multiple samples are grasped.
A conventional method of creating a two-dimensional characteristic data table from three-dimensional spectral data of a plurality of samples will be described. Based on three-dimensional spectral data as shown in FIG. 9A, when the mass spectrum at the retention time RT=0.00, for example, is extracted, the mass spectrum as shown in FIG. 9B is obtained. For such mass spectrum, peak detection is performed according to predetermined conditions, and the height (intensity value) or peak area (integral value of intensity) of each detected peak is obtained. Then, the mass-to-charge ratio and the peak height (or area) of each peak appearing in the mass spectrum are collected as peak information.
By performing the same processing for all mass spectrums obtained over the entire retention time from the start of measurement to the end of measurement, peak information on all peaks appearing in all mass spectrums is obtained. Based on this peak information, as shown in FIG. 10, a two-dimensional characteristic data table showing the peak height (or area) for the mass-to-charge ratio and retention time of each peak is created for each sample. In this table, when there is no peak (not detected) at a certain mass-to-charge ratio and a certain retention time in a certain sample, the peak height corresponding to the mass-to-charge ratio and retention time may be set to zero.
In an LC and a GC (especially LC), even if the composition separation conditions in the column are set to be equal, the retention time of the same component may sometimes somewhat differs due to factors such as the difference in measurement environment and systematic errors of the device, in other words, a retention time shift may sometimes occur. For this reason, prior to creating a two-dimensional characteristic data table according to the procedure as described above, it is sometimes necessary to perform alignment processing in the retention time direction (processing to adjust the retention time) (see Patent Document 1).
Multivariate analysis such as principle component analysis is performed using the two-dimensional characteristic data table created as described above, and based on the result, for example, a large number of samples are classified into a plurality of groups, and further, components characterizing the difference are identified.